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The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design , development and deployment " of a data warehouse or business intelligence system. [ 1 ]
ODM is a vendor-neutral, platform-independent format for interchange and archive of clinical study data. The model includes the clinical data along with its associated metadata, administrative data, reference data and audit information. [8] ODM was first introduced in 1999, and the latest version, 1.3.2, was released in 2012. [9]
PERA is a reference architecture that can model the enterprise in multiple layers and in multiple stages of the architectural life cycle. Initially PERA was part of the PERA methodology, which consisted of three main building blocks: [2] Purdue Enterprise Reference Architecture, Purdue Reference Model, and; Purdue implementation procedures manual
A clinical pathway is a multidisciplinary management tool based on evidence-based practice for a specific group of patients with a predictable clinical course, in which the different tasks (interventions) by the professionals involved in the patient care are defined, optimized and sequenced either by hour (ED), day (acute care) or visit (homecare).
A computerised nursing care plan is a digital way of writing the care plan, compared to handwritten. Computerised nursing care plans are an essential element of the nursing process. [8] Computerised nursing care plans have increased documentation of signs and symptoms, associated factors and nursing interventions. [8]
The ILM policy encompasses storage and information policies that guide management processes. Policies are dictated by business goals and drivers, tying into a framework of overall IT governance and management; change control processes; requirements for system availability and recovery times; and service level agreements (SLAs).
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]
A real-life ETL cycle may consist of additional execution steps, for example: Cycle initiation; Build reference data; Extract (from sources) Validate; Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates) Stage (load into staging tables, if used) Audit reports (for example, on compliance with ...